The Problem with Structured Outputs in LLMs: How Constrained Decoding Creates False Confidence

Summary

This article critiques the use of structured outputs and constrained decoding in large language models (LLMs), arguing that while these techniques appear beneficial for ensuring consistent output formats, they often lead to 'false confidence' by prioritizing output conformance over actual quality. The author explains that constrained decoding forces models to generate outputs that fit predefined schemas, which can result in lower-quality content, factual errors, and misleading confidence in the results. The piece discusses how this approach can mask underlying model limitations and create a false sense of reliability, potentially leading to problematic real-world applications.